code: def __init__(
      self,
      device,
      batch_size,
      iterator,
      layout="HWC",
      num_threads=1,
      device_id=0,
      num_gpus=1,
      axes=(),
      axis_names="",
      align=(),
      shape_arg=(),
  ):
      super().__init__(batch_size, num_threads, device_id, seed=1234)
      self.device = device
      self.layout = layout
      self.iterator = iterator
      self.inputs = ops.ExternalSource()
      self.pad = ops.Pad(
          device=self.device,
          axes=axes,
          axis_names=axis_names,
          align=align,
          shape=shape_arg,
      )
  
imports: import numpy
  import nvidia.dali
  import nvidia.dali.fn
  import nvidia.dali.math
  import nvidia.dali.ops
  import nvidia.dali.types
  from nvidia.dali import Pipeline, pipeline_def
  from nvidia.dali.backend_impl import TensorListGPU
  from nose_utils import assert_raises
  from test_utils import RandomlyShapedDataIterator, generator_random_axes_for_3d_input, generator_random_data, as_array
label_desc: Use image node_id 1 to create a new PadSynthDataPipeline object from inherited base classes: Pipeline with example: obj = PadSynthDataPipeline(device, batch_size, iterator, layout, num_threads, device_id, num_gpus, axes, axis_names, align, shape_arg)